There is a growing interest for Multi-Agent Reinforcement Learning (MARL) as first steps towards building general intelligent agents that learn to make low and high level decisions in non-stationary complex environments in the presence of other agents [1]}, [2]}, [3]}, [4]}, [5]}. Previous results point us towards increased conditions for coordination, efficiency/fairness (see for example [5]} and [7]}, [8]} in which loss averse agents are considered) and common-pool resource sharing [1]}, [8]}.
We further study coordination focusing on how individuals specialise in multitask environments where several rewarding tasks can be performed (as in [4]}). Multitask environments are suited to study general intelligence [12]}: the ability of agents to perform as well as possible in a big variety of tasks.
Agents learning in big multi-agent populations don't necessarily need to perform well in all tasks, but under certain conditions may specialise.
The number of individuals present in the learning may affect or be linked to the cognitive capabilities of each individual (see the relation of these two axis in [13]}).
An example of this can be seen in large social insect communities in which individuals specialize and surprisingly can also switch tasks dynamically whenever needed as in ant colonies [14]}.
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